Joint Average Fusion and Spatiotemporal Registration: Distributed GA-Bernoulli Filtering

This article investigates the problem of distributed target tracking using spatiotemporally unregistered sensor networks, where local sensor nodes operate under heterogeneous temporal and spatial reference frames and inherent disparities in sampling rates, processing latencies, and coordinate systems. All of them contribute to spatiotemporal bias, which imposes a significant challenge for distributed fusion. To address this challenge, we propose a distributed fusion framework, termed geometric average with spatiotemporal registration (GA-STR), which enables joint estimation of both spatiotemporal biases and target states. To ensure identifiability of the posterior of registered parameters, we develop two distinct estimation strategies: first, GA-STR with unknown temporal parameters, designed for nearly constant velocity motion, and second, GA-STR with known temporal parameters. These strategies are subsequently integrated into distributed Bernoulli filtering, where the closed-form solutions are derived. Simulations in an unregistered sensor network for single-target tracking demonstrate that the proposed approach achieves superior estimation accuracy and robustness compared to state-of-the-art approaches.